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山东大学学报 (工学版) ›› 2022, Vol. 52 ›› Issue (2): 1-14.doi: 10.6040/j.issn.1672-3961.0.2021.489

• 机器学习与数据挖掘 •    下一篇

任务粒度视角下的学生成绩预测研究综述

聂秀山1(),马玉玲1,*(),乔慧妍1,郭杰1,崔超然2,于志云,刘兴波1,尹义龙3   

  1. 1. 山东建筑大学计算机科学与技术学院, 山东 济南 250101
    2. 山东财经大学计算机科学与技术学院, 山东 济南 250014
    3. 山东大学软件学院, 山东 济南 250101
  • 收稿日期:2021-10-11 出版日期:2022-04-20 发布日期:2022-04-20
  • 通讯作者: 马玉玲 E-mail:niexsh@hotmail.com;mayuling20@sdjzu.edu.cn
  • 作者简介:聂秀山(1981—)，男，江苏徐州人，教授，博士，主要研究方向为机器学习与数据挖掘。E-mail：niexsh@hotmail.com|聂秀山, 1981年生, 江苏徐州人, 博士, 教授, 山东省泰山学者青年专家, 山东省杰出青年基金获得者, 主要从事机器学习与数据挖掘、教育大数据分析、智能媒体计算方面的研究工作。中国计算机学会(CCF)高级会员, 中国人工智能学会青年工委的常务委员, 山东省人工智能学会常务理事、学术工委秘书长。在TKDE、TIP、IJCAI、软件学报等国内外重要期刊和会议发表论文50余篇
  • 基金资助:
    国家自然科学基金资助项目(62177031);国家自然科学基金资助项目(62077033);山东省自然科学基金资助项目(ZR2021MF044);山东省教育教学研究课题资助项目(2021JXY012);教育部产学合作协同育人项目(202102423045);山东省教育科学“十三五”规划人工智能教育专项一般资助课题(BYZN201905)

Survey on student academic performance prediction from the perspective of task granularity

Xiushan NIE1(),Yuling MA1,*(),Huiyan QIAO1,Jie GUO1,Chaoran CUI2,Zhiyun YU,Xingbo LIU1,Yilong YIN3   

  1. 1. School of Computer Science and Technology, Shandong Jianzhu University, Jinan 250101, Shandong, China
    2. School of Computer Science and Technology, Shandong University of Finance and Economics, Jinan 250014, Shandong, China
    3. School of Software, Shandong University, Jinan 250101, Shandong, China
  • Received:2021-10-11 Online:2022-04-20 Published:2022-04-20
  • Contact: Yuling MA E-mail:niexsh@hotmail.com;mayuling20@sdjzu.edu.cn

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摘要：

学生成绩预测作为教育数据挖掘领域重要的研究分支之一, 学者们已开展了大批卓有成效的研究工作, 但对现有文献进行调查、梳理的综述性研究仍相对缺乏。立足于不同的应用场景, 以学生成绩预测研究的任务粒度为视角, 从答题表现预测、课程成绩预测、综合学习表现预测等3个方面, 详细介绍学生成绩预测研究所采用的技术和方法, 并介绍目前学生成绩预测研究在真实教学场景中的应用情况, 从而为科研和教育管理工作者提供更有针对性的参考信息。

关键词: 教育数据挖掘, 学生学业表现预测, 机器学习, 智慧教育, 个性化教学

Abstract:

As one of the important research branches in educational data mining domain, student performance prediction was intensively studied. However, a comprehensive review of student performance prediction was still underexplored from the perspective of real applications. This paper detailed the technologies and methods exploited in student performance prediction research from the perspective of task granularity, and then introduced several application-oriented cases of student performance prediction, so as to provide targeted reference information for scientific researchers and educators.

Key words: educational data mining, student academic performance prediction, machine learning, intelligent education, individualized teaching

中图分类号: 

  • TP391

图1

贝叶斯知识追踪模型框架"

1 张燕南. 大数据的教育领域应用之研究[D]. 上海: 华东师范大学, 2016.
ZHANG Yannan. A study of big data applications in the field of education[D]. Shanghai: East China Normal University, 2016.
2 张甜, 尹长川, 潘林. 基于改进的聚类和关联规则挖掘的学生成绩分析[J]. 北京邮电大学学报(社会科学版), 2018, 20 (2): 91- 96.
doi: 10.3969/j.issn.1008-7729.2018.02.012
ZHANG Tian , YIN Changchuan , PAN Lin . Score analysis of undergraduate students based on improved clustering and association rules mining[J]. Journal of Beijing University of Posts and Telecommunications (Social Sciences Edition), 2018, 20 (2): 91- 96.
doi: 10.3969/j.issn.1008-7729.2018.02.012
3 ROMERO C , VENTURA S . Educational data mining: a review of the state of the art[J]. IEEE Transactions on Systems Man & Cybernetics Part C, 2010, 40 (6): 601- 618.
4 周庆, 牟超, 杨丹. 教育数据挖掘研究进展综述[J]. 软件学报, 2015, 26 (11): 282- 298.
ZHOU Qing , MU Chao , YANG Dan . Research progress on educational data mining: a survey[J]. Journal of Software, 2015, 26 (11): 282- 298.
5 SMITH M , TRIMBLE H M . The prediction of the future performance of students from their past records[J]. Journal of Chemical Education, 1928, 6 (1): 93.
6 刘孝贤, 王其超. 考试成绩的灰色预测模型[J]. 山东师范大学学报(自然科学版), 1987, (4): 102- 110.
LIU Xiaoxian , WANG Qichao . Grey prediction model for examination marks[J]. Journal of Shandong Normal University (Natural Science), 1987, (4): 102- 110.
7 BADGLEY R F , HETHERINGTON W , MACLEOD J W . Social characteristics and prediction of academic performance of saskatchewan medical students[J]. Canadian Medical Association Journal, 1962, 86 (14): 624- 629.
8 马玉玲. 基于机器学习的高校学生成绩预测方法研究[D]. 济南: 山东大学, 2020.
MA Yuling. Study of college student performance pred-iction based on machine learning[D]. Jinan: Shandong University, 2020.
9 MA Y M, LIU B, WONG C, et al. Targeting the right students using data mining[C]//Processdings of KDD-00. Boston, USA: AAAI Press, 2000: 457-464.
10 SHAHIRI A M , HUSAIN W R , ABDUL N . A review on predicting student's performance using data mining techniques[J]. Procedia Computer Science, 2015, 72, 414- 422.
doi: 10.1016/j.procs.2015.12.157
11 XENOS M , PIERRAKEAS C , PINTELAS P . A survey on student dropout rates and dropout causes concerning the students in the course of informatics of the hellenic open university[J]. Computers & Education, 2002, 39, 361- 377.
12 KUMAR A D , RADHIKA D V . A survey on predicting student performance[J]. International Journal of Computer Science and Information Technologies, 2014, 5 (5): 6147- 6149.
13 周玉龙, 焦广民. 社会环境对中学生影响的调查与分析[C]//全国教育科研"十五"成果论文集: 第一卷. 北京: 新华出版社, 2005: 139-141.
ZHOU Yulong, JIAO Guangmin. Investigation and analysis of the impact of social environment on middle school students[C]//The Proceedings of Achievements of the Tenth Five Year Plan on National Education and Scientific Research: Volume I. Beijing: Xinhua Press, 2005: 139-141.
14 吴增强. 不同学习等第学生家庭环境变量比较研究[J]. 上海教育科研, 1992, (4): 6- 10.
WU Zengqiang . A comparative study on family environment variables of students with different learning grades[J]. Shanghai Research on Education, 1992, (4): 6- 10.
15 张翔, 陈言贵, 赵必华. 家庭环境对学业成绩的影响研究[J]. 宁波大学学报(教育科学版), 2012, 34 (4): 5.
ZHANG Xiang , CHEN Yangui , ZHAO Bihua . A study of the relationship between family's environment and educational achievement[J]. Journal of Ningbo University (Educational Science Edition), 2012, 34 (4): 5.
16 李华, 程晋宽. 校长领导力是如何影响学生成绩的?[J]. 外国教育研究, 2020, 47 (4): 71- 85.
LI Hua , CHENG Jinkuan . How does principal leadership influence student achievement?[J]. Studies in Foreign Education, 2020, 47 (4): 71- 85.
17 ADEDEJI T . The impact of motivation on student's academic achievement and learning outcomes in mathematics among secondary school students in nigeria[J]. Eurasia Journal of Mathematics, Science & Technology Education, 2007, 3 (2): 149- 156.
18 KAUFMAN J C , AGARS M D , LOPEZ-WAGNER M C . The role of personality and motivation in predicting early college academic success in non-traditional students at a hispanic-serving institution[J]. Learning and Individual Differences, 2008, 18 (4): 492- 496.
doi: 10.1016/j.lindif.2007.11.004
19 高秀梅. 当代大学生学习动机的特征及其对学业成绩的影响[J]. 高教探索, 2020, (1): 5.
doi: 10.3969/j.issn.1673-9760.2020.01.002
GAO Xiumei . Characteristics of contemporary college students' learning motivation and its influence on acad-emic performance[J]. Higher Education Exploration, 2020, (1): 5.
doi: 10.3969/j.issn.1673-9760.2020.01.002
20 POROPAT A E . A meta-analysis of the five-factor model of personality and academic performance[J]. Psychological Bulletin, 2009, 135 (2): 322.
doi: 10.1037/a0014996
21 FU J H, CHANG J H, HUANG Y M, et al. A support vector regression-based prediction of students' school performance[C]//Proceedings of IS3C-12. Taiwan, China: IEEE Press, 2012: 84-87.
22 李艳. 专业兴趣吻合度大五人格对大学生学习成绩的影响[J]. 校园心理, 2018, 16 (2): 117- 120.
LI Yan . Consistency of professional interest: the influence of five personality on college students' academic performance[J]. Journal of Campus Life & Mental Health, 2018, 16 (2): 117- 120.
23 赵文学, 王艳芝. 大五人格特质维度与大学生英语学习成绩关系研究[J]. 昌吉学院学报, 2006, (3): 3.
ZHAO Wenxue , WANG Yanzhi . Study on the relationship between the big five personality traits and college students' English learning performance[J]. Journal of Changji College, 2006, (3): 3.
24 O'CONNOR M C , PAUNONEN S V . Big five personality predictors of postsecondary academic performance[J]. Personality & Individual Differences, 2007, 43 (5): 971- 990.
25 PRITCHARD M E , WILSON G S . Using emotional and social factors to predict student success[J]. Journal of College Student Development, 2003, 44 (1): 18- 28.
doi: 10.1353/csd.2003.0008
26 ROBBINS S B , IN-SUE O , HUY L , et al. Intervention effects on college performance and retention as mediated by motivational, emotional, and social control factors: integrated meta-analytic path analyses[J]. Journal of Applied Psychology, 2009, 94 (5): 1163.
doi: 10.1037/a0015738
27 NGUYEN K T , DUONG T M , TRAN N Y , et al. The impact of emotional intelligence on performance: a closer look at individual and environmental factors[J]. The Journal of Asian Finance, Economics and Business, 2020, 7 (1): 183- 193.
doi: 10.13106/jafeb.2020.vol7.no1.183
28 YANG T C , CHEN M C , CHEN S Y . The influences of self-regulated learning support and prior knowledge on improving learning performance[J]. Computers & Education, 2018, 126, 37- 52.
29 MITCHELL T J F , CHEN S Y , MACREDIE R D . Hypermedia learning and prior knowledge: domain expertise vs. system expertise[J]. Journal of Computer Assisted Learning, 2005, 21 (1): 53- 64.
doi: 10.1111/j.1365-2729.2005.00113.x
30 MOOS D C , AZEVEDO R . Self-regulated learning with hypermedia: the role of prior domain knowledge[J]. Contemporary Educational Psychology, 2008, 33 (2): 270- 298.
doi: 10.1016/j.cedpsych.2007.03.001
31 孙睿君, 沈若萌, 管浏斯. 大学生学习成效的影响因素研究[J]. 国家教育行政学院学报, 2012, (9): 65- 71.
doi: 10.3969/j.issn.1672-4038.2012.09.014
SUN Ruijun , SHEN Ruomeng , GUAN Liusi . Study on the influencing factors of college students' learning effectiveness[J]. Journal of National Academy of Education Administration, 2012, (9): 65- 71.
doi: 10.3969/j.issn.1672-4038.2012.09.014
32 FARSHID M , HEIDI A , DIEFES D , et al. Models for early prediction of at-risk students in a course using standards-based grading[J]. Computers & Education, 2016, 103, 1- 15.
33 MAUREEN A C . Aptitude is not enough: how personality and behavior predict academic performance[J]. Journal of Research in Personality, 2006, 40 (3): 339- 346.
doi: 10.1016/j.jrp.2004.10.003
34 BONNARDEL R . Modes of behavior and academic-success at the student level[J]. Travel Humain, 1964, 27 (3/4): 349- 355.
35 CAO Y , GAO J , LIAN D , et al. Orderliness predicts academic performance: behavioral analysis on campus lifestyle[J]. Journal of The Royal Society Interface, 2018, 15 (146): 20180210.
doi: 10.1098/rsif.2018.0210
36 WHEATON A G , CHAPMAN D P , CROFT J B . School start times, sleep, behavioral, health, and academic outcomes: a review of the literature[J]. Journal of School Health, 2016, 86 (5): 363- 381.
doi: 10.1111/josh.12388
37 XU X , WANG J Z , PENG H , et al. Prediction of academic performance associated with internet usage behaviors using machine learning algorithms[J]. Computers in Human Behavior, 2019, 98, 166- 173.
doi: 10.1016/j.chb.2019.04.015
38 SINGLETON R A , WOLFSON A R . Alcohol consumption, sleep, and academic performance among college students[J]. Journal of Studies on Alcohol and Drugs, 2009, 70 (3): 355- 363.
doi: 10.15288/jsad.2009.70.355
39 GILLILAND K , ANDRESS D . Ad lib caffeine consumption, symptoms of caffeinism, and academic performance[J]. The American Journal of Psychiatry, 1981, 138 (4): 512- 515.
doi: 10.1176/ajp.138.4.512
40 LIU Q , WU R Z , CHEN E H , et al. Fuzzy cognitive diagnosis for modelling examinee performance[J]. ACM Transactions on Intelligent Systems and Technology (TIST), 2018, 9 (4): 1- 26.
41 AYERS E , JUNKER B . Irt modeling of tutor performance to predict end-of-year exam scores[J]. Educational & Psychological Measurement, 2007, 68 (6): 972- 987.
42 PARDOS Z A, HEFFERNAN N T. Using hmms and bagged decision trees to leverage rich features of user and skill from an intelligent tutoring system dataset[C]//Processdings of KDD-01. San Francisco, USA: AAAI Press, 2001: 1-16.
43 WU R Z, LIU Q, LIU Y P, et al. Cognitive modelling for predicting examinee performance[C]//Proceedings of IJCAI-15. Buenos Aires, Argentina: Morgan Kaufmann, 2015: 1017-1024.
44 CORBETT A T , ANDERSON J R . K nowledge tracing: modeling the acquisition of procedural knowledge[J]. User Modeling and User-adapted Interaction, 1994, 4, 253- 278.
45 BAKER R S, CORBETT A T, ALEVEN V. More accurate student modeling through contextual estimation of slip and guess probabilities in bayesian knowledge tracing[C]//Proceedings of ITS-08. Montreal, Canada: Springer, 2008: 406-415.
46 PARDOS Z A, HEFFERNAN N T. Modeling individualization in a bayesian networks implementation of knowledge tracing[C]//Proceedings of UMAP-10. Big Island, USA: IEEE Press, 2010: 255-266.
47 YUDELSON M, KOEDINGER R, GORDON J. Individualized bayesian knowledge tracing models[C]//Proceedings of ICAIE-13. Memphis, USA: Springer, 2013: 171-180.
48 CORBETT A , MCLAUGHLIN M , SCARPINATO K C . Modeling student knowledge: Cognitive tutors in high school and college[J]. User Modeling and User-adapted Interaction, 2000, 10, 81- 108.
doi: 10.1023/A:1026505626690
49 PARDOS Z A, HEFFERNAN N T. KT-IDEM: Introducing item difficulty to the knowledge tracing model[C]//Proceedings of UMAP-11. Girona, Spain: ACM Press, 2011: 243-254.
50 NEDUNGADI P, REMYA M. Incorporating forgetting in the personalized, clustered, bayesian knowledge tracing model[C]//Proceedings of ICCSIP-15. Hong Kong, China: IEEE Press, 2015: 1-5.
51 CEN H, KOEDINGER K, JUNKER B. Learning factors analysis-a general method for cognitive model evaluation and improvement[C]//Proceedings of ITS-06. Berlin, Germany: Springer, 2006: 164-175.
52 PAVLIK P I, CEN H, KOEDINGER K R. Performance factors analysis: a new alternative to knowledge tracing[C]//Proceedings of AIED-09. Brighton, UK: Springer, 2009: 531-538.
53 GONG Y , BECK J E , HEFFERNAN N T . How to construct more accurate student models: comparing and optimizing knowledge tracing and performance factor analysis[J]. International Journal of Artificial Intelligence in Education, 2011, 21 (1/2): 27- 46.
54 MANDALAPU V, GONG J, CHEN L. Do we need to go deep? knowledge tracing with big data[J]. arXiv Preprint arXiv: 2101.08349, 2021. https://arxiv.org/ftp/arxiv/papers/2101/2101.08349.pdf.
55 THAI-NGHE N, HORVÁTH T, SCHMIDTTHIEME L. Factorization models for forecasting student performance[C]//Proceedings of ICEDM-10. Pittsburgh, PA, USA: Springer, 2010: 11-20.
56 VIE J J, KASHIMA H. Knowledge tracing machines: factorization machines for knowledge tracing[C]//Proceedings of AAAI-19. Hawaii, USA: AAAI Press, 2019: 750-757.
57 GAN W, SUN Y, YE S, et al. Field-aware knowledge tracing machine by modelling students' dynamic learning procedure and item difficulty[C]//Proceedings of ICDM-19. Beijing, China: IEEE Press, 2019: 1045-1046.
58 CHEN Y Y, LIU Q, HUANG Z Y, et al. Tracking knowledge proficiency of students with educational priors[C]//Proceedings of CIKM-17. Singapore: ACM Press, 2017: 989-998.
59 LIU Q , HUANG Z , YIN Y , et al. EKT: exercise-aware knowledge tracing for student performance prediction[J]. IEEE Transactions on Knowledge and Data Engineering, 2019, 33 (1): 100- 115.
60 BENGIO Y , COURVILLE A , VINCENT P . Representation learning: a review and new perspectives[J]. IEEE Transactions on Pattern Analysis and Machine Intelligence, 2013, 35 (8): 1798- 1828.
doi: 10.1109/TPAMI.2013.50
61 PIECH C, SPENCER J, HUANG J, et al. Deep knowledge tracing[C]// Proceedings of NeurIPS-15. Montreal, Canada: NIPS Press, 2015: 505-513.
62 ZHANG J, SHI X, KING I, et al. Dynamic key-value memory networks for knowledge tracing[C]//Proce-edings of WWW-17. Perth, Australia: ACM Press, 2017: 765-77.
63 PANDEY S, KARYPIS G. A self attentive model for knowledge tracing[C]//Proceedings of ICEDM-19. Montreal, Canada: Springer, 2019: 384-389.
64 SHEN S H, LIU Q, CHEN E H, et al. Convolutional knowledge tracing: modeling individualization in student learning process[C]//Proceedings of SIGIR-20. Xi'an, China. ACM Press, 2020: 1857-1860.
65 YANG Y, SHEN J, QU Y R, et al. GIKT: a graph-based interaction model for knowledge tracing[J]. arXiv Preprint arXiv: 2009, 05991, 2020. https://arxiv.org/pdf/2009.05991.pdf.
66 YEUNG C K. Deep-IRT: make deep learning based knowledge tracing explainable using item response theory[J]. arXiv Preprint arXiv: 1904. 11738, 2019. https://arxiv.org/pdf/1904.11738.pdf.
67 ANAL A , DEVADATTA S . Early prediction of students performance using machine learning techniques[J]. International Journal of Computer Applications, 2014, 107 (1): 37- 43.
doi: 10.5120/18717-9939
68 MARBOUTI F , DIEFES-DUX H A , MADHAVAN K . Models for early prediction of at-risk students in a course using standards-based grading[J]. Computers & Education, 2016, 103, 1- 15.
69 ELBADRAWY A, STUDHAM R S, KARYPIS G. Collaborative multi-regression models for predicting students' performance in course activities[C]//Proceedings of LAK-15. New York, USA: ACM Press, 2015: 103-107.
70 SWEENEY M, LESTER J, RANGWALA H. Next-term student grade prediction[C]//Proceedings of IEEE BigData-15. Santa Clara, CA: IEEE Press, 2015: 970-975.
71 WANG A Y , NEWLIN M H , TUCKER T L . A discourse analysis of online classroom chats: Predictors of cyber-student performance[J]. Teaching of Psychology, 2001, 28 (3): 222- 226.
doi: 10.1207/S15328023TOP2803_09
72 FORTIER M S , VALLERAND R J , FR'ED'ERIC G . Academic motivation and school performance: Toward a structural model[J]. Contemporary Educational Psychology, 1995, 20 (3): 257- 274.
doi: 10.1006/ceps.1995.1017
73 黄建明. 贝叶斯网络在学生成绩预测中的应用[J]. 计算机科学, 2012, 39 (增刊3): 280- 282.
HUANG Jianming . Application of Bayesian network to predicting students' achievement[J]. Computer Science, 2012, 39 (Suppl.3): 280- 282.
74 XING W L , GUO R , EVA P , et al. Participation-based student final performance prediction model through interpretable genetic programming[J]. Computers in Human Behavior, 2015, 47, 168- 181.
doi: 10.1016/j.chb.2014.09.034
75 MA Y M, LIU B, WONG C K, et al. Targeting the right students using data mining[C]//Proceedings of SIGKDD-00. Boston, USA: ACM Press, 2000: 457-464.
76 FANG N, HUANG S B. Work in progress: early prediction of students' academic performance in an introductory engineering course through different mathematical modeling techniques[C]//Proceedings of FIE-12. Seattle, USA: IEEE Press, 2012: 1-2.
77 PANDEY M , SHARMA V K . A decision tree algorithm pertaining to the student performance analysis and prediction[J]. International Journal of Computer Applications, 2013, 61 (13): 1- 5.
doi: 10.5120/9985-4822
78 GEDEON T D, TURNER S. Explaining student grades predicted by a neural network[C]//Proceedings of IJCNN-93. Nagoya, Japan: IEEE Press, 1993: 609-612.
79 LYKOURENTZOU I , GIANNOUKOS I , MPARDIS G , et al. Early and dynamic student achievement prediction in e-learning courses using neural networks[J]. Journal of the American Society for Information Science & Technology, 2014, 60 (2): 372- 380.
80 TAYLAN O , KARAGÖZOĞLU B . An adaptive neuro-fuzzy model for prediction of student's academic performance[J]. Computers & Industrial Engineering, 2009, 57 (3): 732- 741.
81 KIM B H, VIZITEI E, GANAPATHI V. Gritnet: student performance prediction with deep learning[J]. arXiv Preprint arXiv: 1804.07405, 2018. https://arxiv.org/pdf/1804.07405.pdf.
82 罗达雄, 叶俊民, 郭霄宇, 等. Arpdf: 基于对话流的学习者成绩等级预测算法[J]. 小型微型计算机系统, 2019, (2): 6.
LUO Daxiong , YE Junmin , GUO Xiaoyu , et al. Arpdf: achievement rank prediction of learner based on dialogue flow[J]. Journal of Chinese Computer Systems, 2019, (2): 6.
83 AMRA I A, MAGHARI A Y. Students performance prediction using knn and naïve bayesian[C]//Proceedings of ICIT-17. Amman, Jordan: IEEE Press, 2017: 909-913.
84 LOPEZ M I, LUNA J M, ROMERO C, et al. Classification via clustering for predicting final marks based on student participation in forums[C]//Proceedings of ICEDM-12. Chania, Greece: Springer, 2012: 148-151.
85 MINAEI-BIDGOLI B, KASHY D A, KORTMEYER G, et al. Predicting student performance: an application of data mining methods with an educational web-based system[C]//Proceedings of FIE-03. Denver, USA: IEEE Press, 2003: 13-18.
86 ZAFRA A, VENTURA S. Predicting student grades in learning management systems with multiple instance genetic programming[C]//Proceedings of EDM-09. Cordoba, Spain: Springer, 2009: 307-314.
87 MA Y L , CUI C R , NIE X S , et al. Pre-course student performance prediction with multi-instance multi-label learning[J]. Science China Information Sciences, 2019, 62 (2): 29101.
doi: 10.1007/s11432-017-9371-y
88 MOSELEY L G , MEAD D M . Predicting who will drop out of nursing courses: a machine learning exercise[J]. Nurse Education Today, 2008, 28 (4): 469- 475.
doi: 10.1016/j.nedt.2007.07.012
89 MASSA S, PULIAFITO P P. An application of data mining to the problem of the university students' dropout using markov chains[C]//Proceedings of PKDD-99. Prague, Czech Republic: Springer, 1999: 51-60.
90 SANJEEV A P, ZYTKOW J M. Discovering enrollment knowledge in university databases[C]//Proceedings of SIGKDD-95. Montreal, CA: ACM Press, 1995: 246-251.
91 周庆, 尹春梅, 全文君, 等. 基于校园卡消费预测学生挂科情况[J]. 中国教育技术装备, 2017, (24): 51- 54.
doi: 10.3969/j.issn.1671-489X.2017.24.051
ZHOU Qing , YIN Chunmei , QUAN Wenjun , et al. Using consumption of campus card to predict students fail[J]. Educational Equipment in China, 2017, (24): 51- 54.
doi: 10.3969/j.issn.1671-489X.2017.24.051
92 谢娟英, 张宜, 陈恩红. 学生成绩关键因素挖掘与成绩预测[J]. 南京信息工程大学学报(自然科学版), 2019, 11 (3): 316- 325.
XIE Juanying , ZHANG Yi , CHEN Enhong . Key factors mining and prediction of students' performance[J]. Journal of Nanjing University of Information Science and Technology (Natural Science Edition), 2019, 11 (3): 316- 325.
93 LIAN D F , LIU Q . Jointly recommending library books and predicting academic performance: a mutual reinforcement perspective[J]. Journal of Computer Science & Technology, 2018, 33 (4): 654- 667.
94 WANG R, HARARI G M, HAO P L, et al. Smartgpa: how smartphones can assess and predict academic performance of college students[C]// Proceedings of UbiComp-15. Osaka, Japan: ACM Press, 2015: 295-306.
95 YAO H X , LIAN D F , CAO Y , et al. Predicting academic performance for college students: A campus behavior perspective[J]. ACM Transactions on Intelligent Systems and Technology, 2019, 10, 1- 21.
96 MA Y L, ZONG J, CUI C R, et al. Dual path convolutional neural network for student performance prediction[C]//Proceedings of WISE-19. Macau, China: Spr-inger, 2019: 133-146.
97 KONGSAKUN K , FUNG C C . Neural network modeling for an intelligent recommendation system supporting SRM for universities in Thailand[J]. WSEAS Transactions on Computers, 2012, 11 (2): 34- 44.
98 姜强, 赵蔚, 杜欣, 等. 基于用户模型的个性化本体学习资源推荐研究[J]. 中国电化教育, 2010, (5): 106- 111.
doi: 10.3969/j.issn.1006-9860.2010.05.023
JIANG Qiang , ZHAO Wei , DU Xin , et al. Research on personalized ontology learning resource recommendation based on user model[J]. Chinese Audio-visual Education, 2010, (5): 106- 111.
doi: 10.3969/j.issn.1006-9860.2010.05.023
99 YE H J, ZHAN D C, LI X L, et al. College student scholarships and subsidies granting: a multi-modal multi-label approach[C]// Proceedings of ICDM-16. Barce-lona, Spain: IEEE Press, 2016: 559-568.
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